Recently, machine translation has demonstrated significant
progress in terms of translation quality. However, most of the research
has focused on translating with pure monolingual texts in the source
and the target side of the parallel corpora, when in fact code-switching
is very common in communication nowadays. Despite the importance of
handling code-switching in the translation task, existing machine translation systems fail to accommodate the code-switching content. In this
paper, we examine the phenomenon of code-switching in machine translation for low-resource languages. Through different approaches, we evaluate the performance of our systems and make some observations about
the role of code-mixing in the available corpora.

en_IE

dc.description.sponsorship

This publication has emanated from research supported in part by a research
grant from Science Foundation Ireland (SFI) under grant agreement number
SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund,
and the Enterprise Ireland (EI) Innovation Partnership Programme under grant
number IP20180729, NURS – Neural Machine Translation for Under-Resourced
Scenarios.

Files in this item

This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.